Browsing by Author "Deo Prakash Vidyarthi"

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A framework for IoT service selection
(Springer, 2020) Gaurav Baranwal; Manisha Singh; Deo Prakash Vidyarthi
IoT is getting popular as it makes human life comfortable. The industry giants such as IBM, Microsoft, Cisco and Amazon have started offering IoT assistance in form of services. Numerous IoT applications exist today with different roles to play in day-to-day life. Because of application diversity and a good number of IoT service providers, it is difficult for IoT users to select the best one as per the requirement and expected quality of service, QoS. To address this, QoS metrics related to major IoT components, i.e., communication, computing and things, are designed to assess the alternative services. IoT users can express their requirements regarding QoS, while service providers exhibit their offerings. Because of three major IoT components, service selection is considered as multi-criteria group decision-making (MCGDM) problem. This work proposes a new MCGDM framework to rank the IoT services that considers rank reversal problem, judgments of decision makers in linguistic term and the uncertainty and risk-attitudinal characteristics in human decision-making. The proposed framework is validated by comparing it with an existing MCGDM model. A case study on IoT health-care application is provided besides the sensitivity analysis to demonstrate the effectiveness of the proposed framework. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
A genetic task allocation algorithm for distributed computing systems incorporating problem specific knowledge
(World Scientific Publishing Co., 1996) Anil Kumar Tripathi; Deo Prakash Vidyarthi; A.N. Mantri
Distributed Computing Systems (DCS) promise a convenient platform for parallel processing and consequently can be expected to provide highly improved throughput and turnaround characteristics for all types of computing jobs. Task allocation in DCS remains to be an important and relevant problem attracting the attention of researchers in the discipline. Genetic Algorithms (GA) have successfully been used to solve various optimization problems. A GA based task allocation model for multiprocessors has been proposed by Hou, Ansari & Ren [3]. We present a Genetic Task Allocation Algorithm for DCS, wherein we have considered the underlying interconnection network of the processors, communication requirements among modules of the tasks apart from the precedence relation of the task graph that has been considered in [3] also. We have also considered multiprogramming at every processing nodes with related characteristic values. We have, intentionally, made use of the finding [4] that the incorporation of the problem specific knowledge in construction of GAs improves the initial population structures. The model and algorithm proposed by us is sufficiently simple and adequately usable for the purpose of simulation experiments and its possible incorporation in future operating systems of DCS.
A Multi-Criteria Framework for Smart Parking Recommender System
(Institute of Electrical and Electronics Engineers Inc., 2020) Gaurav Baranwal; Dinesh Kumar; Deo Prakash Vidyarthi
Parking has become a real challenge in cities, especially in metros, due to exponential increase in number of vehicles. A significant amount of time wasted in locating the parking space results in traffic congestion, pollution and fuel consumption. Recommending parking spot is an important service towards intelligent transportation system. Evolution in Internet of Things (IoT), Fog and Cloud Computing, and Sensor technologies can be better utilized to explore parking details such as parking occupancy, traffic congestion in parking path etc. in real time and an efficient and effective Parking Recommender System (PRS) can be designed. Parkers may have different expectations from PRS such as walking distance between the destination and the parking spot, pricing, safety etc. Therefore, a personalized recommender system is warranted in which a parker specifies its preference to various quality parameters related to parking. Considering parkers as human being, uncertainty in decision making over the preference cannot be ruled out. This work proposes a framework for multiple quality parameters/criteria based smart parking spot recommender system. It also provides various quality parameters, related to parking, to help parkers to express their need which helps in recommendation. As the boundaries between the parametric values are not crisp, fuzzy logic is utilized in parking recommender method to handle the uncertainty in human decision making. A case study, along with sensitivity analysis, demonstrates the effectiveness of the proposed model. © 2020 IEEE.
A Survey on Auction based Approaches for Resource Allocation and Pricing in Emerging Edge Technologies
(Springer Science and Business Media B.V., 2022) Dinesh Kumar; Gaurav Baranwal; Deo Prakash Vidyarthi
The advancements in sensing technologies, smart devices, wearable gadgets, and communication paradigm enable the vision of the internet of things, smart city, virtual and augmented reality, pervasive healthcare, to name a few. These applications have strict requirements of low latency delivery, high data rate, and instant response. To support this, various new technologies, such as fog computing, mobile edge computing, cloudlet, Micro, and Nano centers, mini and micro clouds, etc., have emerged. The entire set of emerging edge computing paradigms are commonly referred as "edge technologies" in which computational resources and storage are closer to the user/terminal devices somewhere between the device and the cloud data center. The edge technologies aim to deliver computing services with minimal delay by reducing the downward and upward time and data traffic volume. Like cloud service providers, edge service providers are emerging, and a market of edge computing resources has been created. Therefore, Auction theory, a subfield of Economics, is being widely applied for the allocation of resources in emerging edge technologies. This work presents a comprehensive survey on auction-based resource allocation and pricing approaches in emerging edge technologies. An overview of edge technologies and auction theory is given, followed by a thorough review and comparison of the existing auction-based approaches applied in edge technologies for resource allocation and pricing in terms of economic properties. Various open research issues have been deliberated to set the future research direction at the end. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
A survey on nature-inspired techniques for computation offloading and service placement in emerging edge technologies
(Springer, 2022) Dinesh Kumar; Gaurav Baranwal; Yamini Shankar; Deo Prakash Vidyarthi
Internet of Things (IoT) aims to make an environment more innovative and productive by connecting physical things to the internet. Processing generated data from IoT devices and actuation warranted in real-time requires computational infrastructure near the edge to get the outcome without delay. Emerging edge technologies such as Fog computing, Multi-Access Edge Computing, and Cloudlet provide computing resources near the edge, i.e. closer to the IoT devices, where devices can place their services/applications or offload their computational job for processing. The utilization of computing resources provided by emerging edge technologies addresses the issue of delay in the outcome and increases the battery life of IoT devices/End-user devices. Computational resources provided by the edge technologies, i.e. edge/fog nodes, can be heterogeneous, dynamic and mobile. Therefore, service placement and computation offloading on edge/fog nodes are challenging issues, and the problem to finding the best suitable fog/edge nodes is NP-Hard. Nature-inspired algorithms provide robust solutions to NP-Hard problems. Nowadays, nature-inspired algorithms have been widely applied for resource allocation for service placement and computation offloading in emerging edge technologies. In this work, we provide a detailed study on the applications of nature-inspired algorithms in emerging edge computing domains. We provide an overview of emerging edge technologies, related quality parameters and nature-inspired algorithms followed by the basic formulation of service placement and computation offloading in emerging edge computing systems. In this work, we classify the works in emerging edge computing applying nature-inspired algorithms into two categories: works related to service placement and works related to offloading. We provide a thorough review and comparison of the existing nature-inspired approaches in each category. We discuss various open issues at the end to set future research directions. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
A Survey on Spot Pricing in Cloud Computing
(Springer New York LLC, 2018) Dinesh Kumar; Gaurav Baranwal; Zahid Raza; Deo Prakash Vidyarthi
Amazon offers spot instances to cloud customers using an auction-like mechanism. These instances are dynamically priced and offered at a lower price with less guarantee of availability. Observing the popularity of Amazon spot instances among the cloud users, research has intensified on defining the users’ and providers’ behavior in the spot market. This work presents an exhaustive survey of spot pricing in cloud ecosystem. An insight into the Amazon spot instances and its pricing mechanism has been presented for better understanding of the spot ecosystem. Spot pricing and resource provisioning problem, modeled as a market mechanism, is discussed from both computational and economics perspective. A significant amount of important research papers related to price prediction and modeling, spot resource provisioning, bidding strategy designing etc. are summarized and categorized to evaluate the state of the art in the context. All theoretical frameworks, developed for cloud spot market, are illustrated and compared in terms of the techniques and their findings. Finally, research gaps are identified and various economic and computational challenges in cloud spot ecosystem are discussed as a guide to the future research. © 2017, Springer Science+Business Media, LLC, part of Springer Nature.
A truthful combinatorial double auction-based marketplace mechanism for cloud computing
(Elsevier Inc., 2018) Dinesh Kumar; Gaurav Baranwal; Zahid Raza; Deo Prakash Vidyarthi
Designing market-based mechanism that benefits both the cloud customer and cloud provider in a cloud market is a fundamental but complex problem. Double auction is one such mechanism to allocate resources that prevents monopoly and is used to design an unbiased optimal market strategy for cloud market. This work proposes a truthful combinatorial double auction mechanism for allocation and pricing of computing resources in cloud. For resource allocation, utilitarian social welfare maximization problem is formulated using Integer Linear Programming (ILP) and a near optimal solution is obtained using Linear Programming based padded method. For payment, truthful and novel schemes are designed for both customers and providers. Moreover, the proposed mechanism is individual rational, computationally tractable, weakly budget-balance and asymptotic efficient. Performance evaluation and comparative study exhibit that the proposed mechanism is effective on various performance metrics such as utilitarian social welfare, total utility, customers’ satisfaction, providers’ revenue and hence is applicable in real cloud environments. © 2018 Elsevier Inc.
Admission Control Policies in Fog Computing Using Extensive Form Game
(Institute of Electrical and Electronics Engineers Inc., 2022) Gaurav Baranwal; Deo Prakash Vidyarthi
Due to emergence of Internet of Things (IoT), fog computing is gaining momentum in the IT industry. The fog nodes are owned by the fog service providers (FSPs) and usually are not intended to provide their services for free. If FSP charges high for its services or does not stick with its promised quality of service, existing users of that FSP may leave early or may churn to some other FSP. In such competitive scenario, to survive and to maximize the profit in the long run, FSPs should accept the requests of the users considering both technical and non-technical parameters. Since both FSP and IoT users are strategic decision makers, game theoretic analysis may help FSPs to maximize their payoffs. With the change in the strategy of the player, equilibrium solution may change and therefore this dynamic scenario is formulated as an extensive game form. A subgame perfect equilibrium, obtained for this game using backward induction, makes admission control policies suitable for different environment which helps the FSPs in maximizing their profit in the long run. A comparative analysis of the proposed work with state of art indicates that the proposed work outperforms and generates better revenue to the FSPs. © 2013 IEEE.
Allocation aspects in distributed computing systems
(2001) Deo Prakash Vidyarthi; Anil Kumar Tripathi; Biplab Kumer Sarker
Distributed Computing System (DCS) is the natural candidate for the future computing system and in recent past it has received much attention in the computing community. Before the system is fully available for use, certain important issues are to be resolved. An essential phase in Operating System (OS) of the DCS is task allocation, which is an NP-Hard problem. Several task allocation algorithms for Distributed Computing Systems have been reported in the literature[1-5]. These algorithms are based on certain assumptions. We have analysed the implications of the assumptions of these algorithms. Another important issue, in allocation, is the even distribution of the load onto the processing nodes of the DCS (Load balancing). This work aims at identification of a proper task allocation problem model for Distributed Computing System. The work is expected to set guidelines for the researchers of the discipline and get an enlightened consensus over these issues.
Allocation of tasks in a DCS using a different approach with A* considering load
(Institute of Electronics, Information and Communication, Engineers, IEICE, 2004) Biplab Kumer Sarker; Anil Kumar Tripathi; Deo Prakash Vidyarthi; Laurence T. Yang; Kuniaki Uehara
In a Distributed Computing Systems (DCS) tasks submit. ted to it. are usually partitioned into different modules and these modules may be allocated to different processing nodes so as to achieve minimum turn around time of the tasks utilizing the maximum resources of the existing system such as CPU speed, memory capacities etc. The problem lies on how to obtain the optimal allocation of these multiple tasks by keeping in mind that no processing node is overloaded due to this allocation. This paper proposes an algorithm A*RS, using well-known A*, which aims to reduce the search space and time for task allocation. It aims at minimization of turn around time of tasks in the way so that processing nodes do not become overloaded due to this allocation. Our experimental results justify the claims with necessary supports by comparing it with the earlier algorithm for multiple tasks allocation.
Auction based resource provisioning in cloud computing
(Springer, 2018) Gaurav Baranwal; Dinesh Kumar; Zahid Raza; Deo Prakash Vidyarthi
[No abstract available]
Auction theory
(Springer, 2018) Gaurav Baranwal; Dinesh Kumar; Zahid Raza; Deo Prakash Vidyarthi
Auctioning has a long history and is reported to have been used in Babylon as early as 500 B.C. The entire Roman Empire was sold off using auction in 193 A.D. [83]. With time, auction theory has evolved with more sophisticated and mature auction procedures. Auction is considered as an efficient and fair mechanism as it provides equal opportunity to both the seller and the buyer. The price of the resources is decided on the basis of the value of the resources for the bidders that makes higher revenue. In early auction days and even during its evolution, only antiques and art matters were sold using auction but now various commodities, e.g. fish, bond, spectrum, computing resources, are sold using auction because of its multifaceted benefits. After the introduction of mechanism design, auction has become a great success in economics for the resource allocation. Mechanism design and information and communication technologies (ICTs) are the two major responsible factors that made game theory and optimization an effective tool for auction design to achieve specific goals. In economics, the rich literature and practical implementation on auction are available. Because of involvement of pricing in resource allocation in Cloud computing, good possibilities can be explored for applying auction in Cloud computing. Academicians have proposed various types of auction models that can be applied in different scenarios in Cloud computing. Spot market has been become a milestone for both, academicians and professionals, to explore auction in greater depth. Amazon, a giant in the Cloud computing market, practically gave a push to implement dynamic pricing using auction. Plenty of works on auction theory, variants of auction as well as its applications for suitable scenario are available in the form of books, survey papers, etc. The aim of this chapter is to provide a detailed description of most important findings of the auction so that academicians and researchers can work their way through these findings in Cloud computing. © The Author(s) 2018.
BARA: A blockchain-aided auction-based resource allocation in edge computing enabled industrial internet of things
(Elsevier B.V., 2022) Gaurav Baranwal; Dinesh Kumar; Deo Prakash Vidyarthi
The recent emergence of Industrial Internet of Things (IIoT), a novel subset of Internet of Things (IoT), integrates sensors and intelligent devices with industry applications to develop a self-organizing system for creating enhanced and adaptive industrial environments. IIoT devices usually have limited computational power, while IIoT applications are mostly mission-critical and/or safety-critical. Therefore, these applications need computing resources that are closer to the devices. This work proposes a decentralized auction-based resource allocation mechanism in edge computing enabled IIoT using consortium blockchain and smart contract that shelves the involvement of a trusted third party, i.e. auctioneer. Various quality parameters are considered during resource allocation to address the mobility of both IIoT devices and edge resources, heterogeneity of edge servers, false assurance of edge servers, reliability, delay in results, and responsiveness of edge servers. Utility functions for IIoT devices are designed to calculate their degree of satisfaction depending on various quality parameters. The proposed blockchain-aided auction based mechanism fulfills various auction-based resource allocation requirements such as seal bidding, no impersonation of the bidder, no modification in any bid or result of allocation by the auctioneer, and proof for winners. The proposed work encourages edge servers for truthful bidding and furnishes the allocation results in polynomial time. Performance evaluation of the proposed model exhibits encouraging results. © 2022 Elsevier B.V.
Blockchain based resource allocation in cloud and distributed edge computing: A survey
(Elsevier B.V., 2023) Gaurav Baranwal; Dinesh Kumar; Deo Prakash Vidyarthi
Cloud computing and distributed edge computing provide computing resources to meet the surging demands for computing caused by developments in technologies such as the Internet of Things (IoT) and Mobile communication (5G). Centralized resource allocation approaches in both computing paradigms suffer from single-point failure, tampering, modification in allocation results, and biased actions. Recently, blockchain has become popular in designing decentralized systems because of its features, including transparency, decentralization, and anti-tamper. In this paper, we provide a comprehensive survey of the research works applying blockchain in resource allocation in both computing paradigms, addressing the issues in centralized resource allocation approaches. Firstly, we identify several key research questions acting as motivation. To provide background knowledge, first, we discuss the centralized resource allocation in both computing paradigms and associated challenges. Then we discuss blockchain, its structure, working, characteristics and types, followed by its benefits to resource allocation. We identify several metrics to provide a comparative study of the works. We present a depth overview of blockchain-based resource allocation works in three domains: cloud computing, distributed edge computing and integrated edge and cloud computing. In each domain, works are summarized from three aspects: works using blockchain platforms, works providing blockchain frameworks and works advocating blockchain. We discuss consensus mechanisms in the works related to blockchain-based resource allocation, as the consensus mechanism is a fundamental part of the blockchain. Further, we provide key challenges requiring our attention. Finally, we conclude the survey. © 2023 Elsevier B.V.
Cluster-based multiple task allocation in distributed computing system
(2004) Deo Prakash Vidyarthi; Anil Kumar Tripathi; Biplab Kumer Sarker; Abhishek Dhawan; Laurence Tianruo Yang
Most of the task allocation models & algorithms in Distributed Computing System (DCS) require a priori knowledge of its execution time on the processing nodes. Since the task assignment is not known in advance, this time is quite difficult to estimate. We propose a cluster-based dynamic allocation scheme, in a distributed computing system, which eliminate this time requirement. Further, as opposed to a single task allocation, generally proposed in most of the models, we consider multiple tasks. A fuzzy function is used for both the module clustering and processor clustering. Dynamic invocation of clustering and assignment is considered. Experimental results show the efficacy of the proposed model.
Comparative Study of Two GA Based Task Allocation Models in Distributed Computing System
(2003) Deo Prakash Vidyarthi; Anil Kumar Tripathi; Biplab Kumar Sarker; Kirti Rani
Genetic Algorithm has emerged as a successful tool for optimization problems. Earlier, we proposed a task allocation model to maximize the reliability of Distributed Computing System(DCS) using genetic algorithm. Our objective, in this work, is to use the same simple genetic algorithm to minimize the turnaround time of the task given to the DCS for execution and then to compare the resultant allocation with the allocation of maximal reliablity as obtained in [2]. Comparisons of both the algorithms are made by illustrated examples and appropriate comments.
Computation offloading model for smart factory
(Springer Science and Business Media Deutschland GmbH, 2021) Gaurav Baranwal; Deo Prakash Vidyarthi
Internet of Things (IoT) is coming up in a rapid pace in various application domains. In smart factories, IoT can be deployed using sensors and actuators for taking smart manufacturing decisions. To maintain the smartness, huge computational power is required to handle the generated data by the IoT sensors. Local servers, in smart factories, usually organize the sensors/actuators and takes decisions at the local level. However, they are not equipped with enough computational power to handle all types of computational tasks and therefore some tasks need to be offloaded to the upper layer such as Cloud. Hybrid cloud i.e. public cloud along with some local servers can better handle this requirement. Recently, an additional layer called fog computing is introduced in the cloud architecture to complement it with added power. Offloading of tasks, generated by the industrial applications of IoT devices, should be done only when existing computational power of local server is not able to meet the quality requirements of the tasks. An ultimate objective of the smart factory owner is to earn revenue and for that, IoT devices need to meet their quality of service expectation. For offloading, tasks can be categorized as delay sensitive and delay tolerant and making decision on offloading by the local server is non-trivial. This work proposes an offloading decision model using game theory in a non-cooperative environment considering the categorization of tasks and it is shown that dominant strategy exists for the local server. For the performance study of the proposed model, simulation is done using iFogSim simulator. A comparative study with state-of-art exhibits that the proposed offloading scheme outperforms. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
Double auction-based cloud resource provisioning
(Springer, 2018) Gaurav Baranwal; Dinesh Kumar; Zahid Raza; Deo Prakash Vidyarthi
In double auction, both the user and the provider bid in the market. In this chapter, a detailed description of double auction mechanisms and their applicability in Cloud market is discussed. Various auction properties, that a double auction mechanism should satisfy, have also been discussed and deliberated. A detailed survey of related work in double auction-based Cloud resource provisioning and pricing is provided. A truthful multi-unit double auction-based model is also presented to help and motivate the Cloud researchers/academicians to design such efficient and truthful mechanisms for the Cloud market. Finally, research challenges and issues are identified with future research possibilities. © The Author(s) 2018.
Epilogue
(Springer, 2018) Gaurav Baranwal; Dinesh Kumar; Zahid Raza; Deo Prakash Vidyarthi
Amazon provides virtual machines using auction called spot instances besides providing virtual machines with on-demand and reserved policy. Spot instances are real implementation of auction in Cloud computing. In this chapter, first a brief description of Amazon spot market is given to make readers understand that whatever they learnt from this book are going to fetch enormous feasible opportunities in research. The chapter briefly recalls key concepts and key learning points, obtained from this book, along with summarization. Auction itself has a rich literature, and it has not been applied only in economics but in areas in Computer Science also. This chapter brings forth various literature related to auction and its application in other fields of Computer Science for further research. © The Author(s) 2018.
Fair mechanisms for combinatorial reverse auction-based cloud market
(Springer Science and Business Media Deutschland GmbH, 2019) Dinesh Kumar; Gaurav Baranwal; Zahid Raza; Deo Prakash Vidyarthi
Cloud computing is a business model of computing. Cloud providers started offering computing resources using various market-based allocation mechanisms. In combinatorial reverse auction-based cloud market, a user has computing resource requirements in the form of bundle, and provider also provides the resources in the bundle form. In this work, to ensure healthy competition in the cloud market, priority-based fair mechanisms have been proposed for combinatorial reverse auction-based cloud market along with a discussion on their advantages, shortcomings, applicability, etc. The proposed mechanisms are compared in terms of provider’s revenue, the number of winning providers, etc. © Springer Nature Singapore Pte Ltd. 2019.